We present the first longitudinal study of pressure sensing to infer real-world water usage events in the home (e.g., dishwasher, upstairs bathroom sink, downstairs toilet). In order to study the pressure-based approach out in the wild , we deployed a ground truth sensor network for five weeks in three homes and two apartments that directly monitored valve-level water usage by fixtures and appliances . We use this data to, first, demonstrate the practical challenges in constructing water usage activity inference algorithms and, second, to inform the design of a new probabilistic-based classification approach. Inspired by algorithms in speech recognition, our novel Bayesian approach incorporates template matching, a language model, grammar, and prior probabilities. We show that with a single pressure sensor, our probabilistic algorithm can classify real-world water usage at the fixture level with 90% accuracy and at the fixturecategory level with 96% accuracy. With two pressure sensors, these accuracies increase to 94% and 98%. Finally, we show how our new approach can be trained with fewer examples than a strict template-matching approach alone.